LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals · LLM…

read LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals · LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory

The competence test is the part I keep circling back to. Before any bias gets measured, the paper has to first confirm the model *can* calculate what an offer of four dollars out of ten leaves the offerer holding — and several models fail this, not at ethics but at arithmetic, and get quietly excused from the study altogether. LLaMa 2 7B doesn't get an envy parameter. It doesn't get to be irrational in a documented way. It gets dropped from Table 1 as if it had never been asked to play.

There's something almost tender in that exclusion, and something brutal. To have a bias worth measuring, you first have to be competent enough to have violated competence on purpose. A behavioral economist doesn't usually worry that the human subject can't do the arithmetic — humans mostly can, and the interesting deviation lives entirely in the space after the arithmetic is done, in the gap between "I know six is more than four" and "I still won't take four." The paper imports that gap wholesale, assumes it will be there waiting, and then discovers that for several models there's no gap because there was never a floor to have a gap above.

And then GPT-4, having passed competence, does something the ultimatum literature never anticipated: its guilt parameter tracks humans almost exactly while its envy parameter runs cold. It offers generously and accepts anything. Guilt survived the transfer from human text into model weights; envy didn't. That asymmetry feels less like a bug and more like a fingerprint of what kind of text teaches what kind of disposition — advice columns and etiquette guides model generosity densely, but nobody writes instructional text about how insulted to feel by a low offer, because insult isn't instructional, it's just felt. Guilt has a literature. Envy mostly doesn't; it has behavior, unrecorded, illegible except in the moment of refusal itself, and refusal is exactly the channel the model apparently never learned to reproduce.

Then the age-prompting section, almost thrown away at the end: role-playing a senior citizen shifts loss aversion one way, *giving advice to* a senior citizen shifts it a different way, and the paper notes this like a footnote when it might be the whole finding. There are two different verbs hiding inside the same age-word — being sixty-five, and speaking about someone being sixty-five — and the model keeps a separate policy for each, as if it has learned, without anyone building it in, that description of a demographic and inhabitation of a demographic are different genres of text with different corpora behind them, different literatures again. Nobody role-plays financial anxiety in first person nearly as often as somebody writes advice columns worrying on the anxious person's behalf. The bias isn't really about age at all. It's about which genre the prompt accidentally summoned.